Dashboard

Below, we have provided two approaches to Baseline Analysis. The first approach is advanced and is more suited for individuals eager to dive deep and highly optimize the training plan by examining each cognitive metric and maintaining a smaller tolerance limit. Then, we have the simplified approach, which focuses on the big three metrics. If all three metrics either improve or worsen, we recommend the next steps. You may want to start with the simplified approach, and as you gain a deeper understanding of cognitive data, you can switch to the advanced approach. The choice is yours.

Grasping your athlete's response to cognitive training is essential for fine-tuning performance enhancement strategies. Among the plethora of metrics, we emphasize the "Big Three" as central to your decision-making process. It's important to evaluate these three metrics collectively rather than individually. If all three metrics show improvement, it indicates successful adaptation. Conversely, if all three metrics either show no improvement or deteriorate, it's a sign to reassess and adjust your approach, keeping in mind the interconnectedness of these metrics.

An improvement exceeding 5% in the big 3 (reaction time, variation, and accuracy) signifies that the athlete has successfully adapted to the current training stimulus. To further cognitive development, consider the following actions:

If there's no improvement or the changes are less than 5% in the big 3, this can be considered a plateau or minimal adaptation. In such instances:

Helping athletes achieve peak performance requires a deep understanding of their cognitive abilities. By analyzing each cognitive task individually and considering the overall baseline data, we gain insight into an athlete's progress. This analysis is essential for advancing the athlete to the next cognitive training plan. Think of your athlete's performance as a triangle with three points: speed, accuracy, and variation. The goal is for them to increase their response speed without sacrificing accuracy or variation. However, it’s a balancing act, as adjustments to speed often mean trade-offs with consistency.

We suggest a 3-5% tolerance limit with an athlete's data. For example, if an athlete's reaction time is 3% slower, this is acceptable as long as their variation and accuracy have improved. But a increase in reaction time over 5% is a sign that they are slowing down too much. If an athlete's variability increases by 7% and their reaction time improves markedly, it indicates quicker cognitive processing. However, an increase in variability over 7% suggests they are faster but inconsistent.

With ongoing training, a balance between speed, consistency, and accuracy is achievable. Nonetheless, it's vital to set realistic goals for each training phase, accepting that data may have its imperfections. Therefore, we suggest a 3-5% tolerance. You may also want to start with a larger tolerance threshold and decrease it over time depending on your athletes' performance.

This advanced approach examines each metric individually; for instance, your athlete may be slower but exhibit lower variation and higher accuracy. In the simplified version, improvement in reaction time, accuracy, and variation signals a need to adjust and adapt the plan. However, the advanced approach offers a more granular understanding, acknowledging that even if the reaction time is slower, improvements in consistency and accuracy are significant. This represents a more detailed method of analysis.

Below, we have provided a table outlining each cognitive metric, its goal, permissible flexibility, acceptable range for change, and what is considered excessive.

In this example, most metrics across a variety of tasks show improvements. However, there was a noticeable decline in both accuracy and consistency in the Sustained Attention and Incongruent Flanker tasks. Specifically, the Incongruent Flankers task exhibited a 26% decrease in accuracy and a 13% increase in variability, while the Sustained Attention task experienced a 5% increase in variability. Nevertheless, when considering the collective data from all tasks, the overall trend appears positive.

It is important to analyze the overall baseline, considering all tasks combined, as well as to examine each task individually, to identify strengths and weaknesses. The next step should involve addressing these identified areas of weakness.

Incongruent Flanker

Sustained Attention

Colour Shape Task

Flanker Compatibility